Overview

Brought to you by YData

Dataset statistics

Number of variables44
Number of observations13294
Missing cells123072
Missing cells (%)21.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 MiB
Average record size in memory352.0 B

Variable types

Numeric15
Categorical22
Text7

Alerts

fg_missed_0_19 has constant value "0.0"Constant
fg_att is highly overall correlated with fg_made and 1 other fieldsHigh correlation
fg_blocked is highly overall correlated with fg_blocked_distance and 1 other fieldsHigh correlation
fg_blocked_distance is highly overall correlated with fg_blocked and 1 other fieldsHigh correlation
fg_long is highly overall correlated with fg_made_40_49 and 3 other fieldsHigh correlation
fg_made is highly overall correlated with fg_att and 1 other fieldsHigh correlation
fg_made_40_49 is highly overall correlated with fg_long and 1 other fieldsHigh correlation
fg_made_60_ is highly overall correlated with fg_longHigh correlation
fg_made_distance is highly overall correlated with fg_att and 3 other fieldsHigh correlation
fg_missed is highly overall correlated with fg_missed_distance and 2 other fieldsHigh correlation
fg_missed_30_39 is highly overall correlated with fg_missed_distanceHigh correlation
fg_missed_40_49 is highly overall correlated with fg_missed_distanceHigh correlation
fg_missed_50_59 is highly overall correlated with fg_missed_distanceHigh correlation
fg_missed_60_ is highly overall correlated with gwfg_distanceHigh correlation
fg_missed_distance is highly overall correlated with fg_missed and 6 other fieldsHigh correlation
fg_pct is highly overall correlated with fg_missed and 3 other fieldsHigh correlation
gwfg_blocked is highly overall correlated with fg_blocked and 1 other fieldsHigh correlation
gwfg_distance is highly overall correlated with fg_long and 1 other fieldsHigh correlation
gwfg_made is highly overall correlated with fg_missed_distance and 2 other fieldsHigh correlation
gwfg_missed is highly overall correlated with fg_missed and 3 other fieldsHigh correlation
pat_att is highly overall correlated with pat_madeHigh correlation
pat_made is highly overall correlated with pat_attHigh correlation
pat_missed is highly overall correlated with pat_pctHigh correlation
pat_pct is highly overall correlated with pat_missedHigh correlation
position is highly overall correlated with position_groupHigh correlation
position_group is highly overall correlated with positionHigh correlation
season_type is highly overall correlated with weekHigh correlation
week is highly overall correlated with season_typeHigh correlation
season_type is highly imbalanced (74.7%)Imbalance
position is highly imbalanced (98.7%)Imbalance
position_group is highly imbalanced (99.6%)Imbalance
fg_missed is highly imbalanced (54.7%)Imbalance
fg_blocked is highly imbalanced (82.5%)Imbalance
fg_made_0_19 is highly imbalanced (89.1%)Imbalance
fg_made_50_59 is highly imbalanced (69.8%)Imbalance
fg_made_60_ is highly imbalanced (97.2%)Imbalance
fg_missed_20_29 is highly imbalanced (93.2%)Imbalance
fg_missed_30_39 is highly imbalanced (78.3%)Imbalance
fg_missed_40_49 is highly imbalanced (67.9%)Imbalance
fg_missed_50_59 is highly imbalanced (70.4%)Imbalance
fg_missed_60_ is highly imbalanced (94.1%)Imbalance
pat_missed is highly imbalanced (84.3%)Imbalance
pat_blocked is highly imbalanced (92.6%)Imbalance
gwfg_att is highly imbalanced (71.9%)Imbalance
gwfg_blocked is highly imbalanced (79.6%)Imbalance
player_name has 5953 (44.8%) missing valuesMissing
headshot_url has 5364 (40.3%) missing valuesMissing
fg_made has 1640 (12.3%) missing valuesMissing
fg_missed has 1640 (12.3%) missing valuesMissing
fg_blocked has 1640 (12.3%) missing valuesMissing
fg_long has 2493 (18.8%) missing valuesMissing
fg_pct has 1640 (12.3%) missing valuesMissing
fg_made_0_19 has 1640 (12.3%) missing valuesMissing
fg_made_20_29 has 1640 (12.3%) missing valuesMissing
fg_made_30_39 has 1640 (12.3%) missing valuesMissing
fg_made_40_49 has 1640 (12.3%) missing valuesMissing
fg_made_50_59 has 1640 (12.3%) missing valuesMissing
fg_made_60_ has 1640 (12.3%) missing valuesMissing
fg_missed_0_19 has 1640 (12.3%) missing valuesMissing
fg_missed_20_29 has 1640 (12.3%) missing valuesMissing
fg_missed_30_39 has 1640 (12.3%) missing valuesMissing
fg_missed_40_49 has 1640 (12.3%) missing valuesMissing
fg_missed_50_59 has 1640 (12.3%) missing valuesMissing
fg_missed_60_ has 1640 (12.3%) missing valuesMissing
fg_made_list has 2493 (18.8%) missing valuesMissing
fg_missed_list has 9895 (74.4%) missing valuesMissing
fg_blocked_list has 12762 (96.0%) missing valuesMissing
fg_made_distance has 1640 (12.3%) missing valuesMissing
fg_missed_distance has 1640 (12.3%) missing valuesMissing
fg_blocked_distance has 1640 (12.3%) missing valuesMissing
pat_made has 1167 (8.8%) missing valuesMissing
pat_missed has 1167 (8.8%) missing valuesMissing
pat_blocked has 1167 (8.8%) missing valuesMissing
pat_pct has 1167 (8.8%) missing valuesMissing
gwfg_distance has 12071 (90.8%) missing valuesMissing
gwfg_made has 12071 (90.8%) missing valuesMissing
gwfg_missed has 12071 (90.8%) missing valuesMissing
gwfg_blocked has 12071 (90.8%) missing valuesMissing
fg_made has 853 (6.4%) zerosZeros
fg_att has 1640 (12.3%) zerosZeros
fg_pct has 853 (6.4%) zerosZeros
fg_made_20_29 has 6539 (49.2%) zerosZeros
fg_made_40_49 has 6988 (52.6%) zerosZeros
fg_made_distance has 853 (6.4%) zerosZeros
fg_missed_distance has 8255 (62.1%) zerosZeros
fg_blocked_distance has 11122 (83.7%) zerosZeros
pat_made has 144 (1.1%) zerosZeros
pat_att has 1167 (8.8%) zerosZeros
pat_pct has 144 (1.1%) zerosZeros

Reproduction

Analysis started2024-08-09 02:46:00.373324
Analysis finished2024-08-09 02:46:24.926769
Duration24.55 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

season
Real number (ℝ)

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.1164
Minimum1999
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.0 KiB
2024-08-08T20:46:24.983821image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1999
5-th percentile2000
Q12005
median2011
Q32017
95-th percentile2022
Maximum2023
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2337556
Coefficient of variation (CV)0.0035968855
Kurtosis-1.2095101
Mean2011.1164
Median Absolute Deviation (MAD)6
Skewness-0.0089633985
Sum26735782
Variance52.327221
MonotonicityIncreasing
2024-08-08T20:46:25.088959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2022 565
 
4.3%
2023 563
 
4.2%
2021 561
 
4.2%
2020 539
 
4.1%
2003 534
 
4.0%
2004 533
 
4.0%
2005 533
 
4.0%
2010 532
 
4.0%
2002 531
 
4.0%
2008 531
 
4.0%
Other values (15) 7872
59.2%
ValueCountFrequency (%)
1999 519
3.9%
2000 506
3.8%
2001 516
3.9%
2002 531
4.0%
2003 534
4.0%
2004 533
4.0%
2005 533
4.0%
2006 526
4.0%
2007 530
4.0%
2008 531
4.0%
ValueCountFrequency (%)
2023 563
4.2%
2022 565
4.3%
2021 561
4.2%
2020 539
4.1%
2019 528
4.0%
2018 524
3.9%
2017 525
3.9%
2016 531
4.0%
2015 528
4.0%
2014 527
4.0%

week
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5660448
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.0 KiB
2024-08-08T20:46:25.191062image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q314
95-th percentile17
Maximum22
Range21
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.3173389
Coefficient of variation (CV)0.55585552
Kurtosis-1.1471292
Mean9.5660448
Median Absolute Deviation (MAD)5
Skewness0.02330704
Sum127171
Variance28.274093
MonotonicityNot monotonic
2024-08-08T20:46:25.291153image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
17 787
 
5.9%
2 785
 
5.9%
16 785
 
5.9%
15 783
 
5.9%
1 782
 
5.9%
12 777
 
5.8%
13 771
 
5.8%
14 767
 
5.8%
3 762
 
5.7%
11 736
 
5.5%
Other values (12) 5559
41.8%
ValueCountFrequency (%)
1 782
5.9%
2 785
5.9%
3 762
5.7%
4 725
5.5%
5 711
5.3%
6 693
5.2%
7 688
5.2%
8 694
5.2%
9 682
5.1%
10 708
5.3%
ValueCountFrequency (%)
22 6
 
< 0.1%
21 55
 
0.4%
20 113
 
0.9%
19 210
 
1.6%
18 274
 
2.1%
17 787
5.9%
16 785
5.9%
15 783
5.9%
14 767
5.8%
13 771
5.8%

season_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.0 KiB
REG
12732 
POST
 
562

Length

Max length4
Median length3
Mean length3.0422747
Min length3

Characters and Unicode

Total characters40444
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREG
2nd rowREG
3rd rowREG
4th rowREG
5th rowREG

Common Values

ValueCountFrequency (%)
REG 12732
95.8%
POST 562
 
4.2%

Length

2024-08-08T20:46:25.399255image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:25.487335image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
reg 12732
95.8%
post 562
 
4.2%

Most occurring characters

ValueCountFrequency (%)
R 12732
31.5%
E 12732
31.5%
G 12732
31.5%
P 562
 
1.4%
O 562
 
1.4%
S 562
 
1.4%
T 562
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 12732
31.5%
E 12732
31.5%
G 12732
31.5%
P 562
 
1.4%
O 562
 
1.4%
S 562
 
1.4%
T 562
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 12732
31.5%
E 12732
31.5%
G 12732
31.5%
P 562
 
1.4%
O 562
 
1.4%
S 562
 
1.4%
T 562
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 12732
31.5%
E 12732
31.5%
G 12732
31.5%
P 562
 
1.4%
O 562
 
1.4%
S 562
 
1.4%
T 562
 
1.4%
Distinct202
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size104.0 KiB
2024-08-08T20:46:25.691832image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters132940
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)0.2%

Sample

1st row00-0000108
2nd row00-0000108
3rd row00-0000108
4th row00-0000108
5th row00-0000108
ValueCountFrequency (%)
00-0016919 341
 
2.6%
00-0004091 301
 
2.3%
00-0019646 286
 
2.2%
00-0023252 281
 
2.1%
00-0025580 279
 
2.1%
00-0023853 255
 
1.9%
00-0020578 250
 
1.9%
00-0000108 247
 
1.9%
00-0024333 247
 
1.9%
00-0025565 231
 
1.7%
Other values (192) 10576
79.6%
2024-08-08T20:46:26.024393image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 62506
47.0%
- 13294
 
10.0%
2 10160
 
7.6%
3 8513
 
6.4%
1 7236
 
5.4%
9 6546
 
4.9%
6 5984
 
4.5%
8 5461
 
4.1%
5 4853
 
3.7%
7 4732
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 132940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 62506
47.0%
- 13294
 
10.0%
2 10160
 
7.6%
3 8513
 
6.4%
1 7236
 
5.4%
9 6546
 
4.9%
6 5984
 
4.5%
8 5461
 
4.1%
5 4853
 
3.7%
7 4732
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 132940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 62506
47.0%
- 13294
 
10.0%
2 10160
 
7.6%
3 8513
 
6.4%
1 7236
 
5.4%
9 6546
 
4.9%
6 5984
 
4.5%
8 5461
 
4.1%
5 4853
 
3.7%
7 4732
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 132940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 62506
47.0%
- 13294
 
10.0%
2 10160
 
7.6%
3 8513
 
6.4%
1 7236
 
5.4%
9 6546
 
4.9%
6 5984
 
4.5%
8 5461
 
4.1%
5 4853
 
3.7%
7 4732
 
3.6%

team
Categorical

Distinct32
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size104.0 KiB
NE
 
444
PHI
 
436
BAL
 
436
PIT
 
430
SEA
 
429
Other values (27)
11119 

Length

Max length3
Median length3
Mean length2.7456747
Min length2

Characters and Unicode

Total characters36501
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPHI
2nd rowPHI
3rd rowPHI
4th rowPHI
5th rowPHI

Common Values

ValueCountFrequency (%)
NE 444
 
3.3%
PHI 436
 
3.3%
BAL 436
 
3.3%
PIT 430
 
3.2%
SEA 429
 
3.2%
GB 429
 
3.2%
IND 429
 
3.2%
SF 424
 
3.2%
KC 423
 
3.2%
NO 421
 
3.2%
Other values (22) 8993
67.6%

Length

2024-08-08T20:46:26.144765image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ne 444
 
3.3%
bal 436
 
3.3%
phi 436
 
3.3%
pit 430
 
3.2%
sea 429
 
3.2%
gb 429
 
3.2%
ind 429
 
3.2%
sf 424
 
3.2%
kc 423
 
3.2%
no 421
 
3.2%
Other values (22) 8993
67.6%

Most occurring characters

ValueCountFrequency (%)
A 4569
12.5%
N 3779
 
10.4%
I 3336
 
9.1%
L 2908
 
8.0%
E 2515
 
6.9%
C 2472
 
6.8%
T 2088
 
5.7%
B 1692
 
4.6%
D 1666
 
4.6%
S 1256
 
3.4%
Other values (14) 10220
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36501
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4569
12.5%
N 3779
 
10.4%
I 3336
 
9.1%
L 2908
 
8.0%
E 2515
 
6.9%
C 2472
 
6.8%
T 2088
 
5.7%
B 1692
 
4.6%
D 1666
 
4.6%
S 1256
 
3.4%
Other values (14) 10220
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36501
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4569
12.5%
N 3779
 
10.4%
I 3336
 
9.1%
L 2908
 
8.0%
E 2515
 
6.9%
C 2472
 
6.8%
T 2088
 
5.7%
B 1692
 
4.6%
D 1666
 
4.6%
S 1256
 
3.4%
Other values (14) 10220
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36501
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4569
12.5%
N 3779
 
10.4%
I 3336
 
9.1%
L 2908
 
8.0%
E 2515
 
6.9%
C 2472
 
6.8%
T 2088
 
5.7%
B 1692
 
4.6%
D 1666
 
4.6%
S 1256
 
3.4%
Other values (14) 10220
28.0%

player_name
Text

MISSING 

Distinct101
Distinct (%)1.4%
Missing5953
Missing (%)44.8%
Memory size104.0 KiB
2024-08-08T20:46:26.320937image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length13
Median length12
Mean length8.5015665
Min length5

Characters and Unicode

Total characters62410
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)0.2%

Sample

1st rowP.Dawson
2nd rowP.Dawson
3rd rowP.Dawson
4th rowP.Dawson
5th rowP.Dawson
ValueCountFrequency (%)
a.vinatieri 341
 
4.6%
p.dawson 301
 
4.1%
s.janikowski 286
 
3.9%
r.gould 281
 
3.8%
m.crosby 279
 
3.8%
m.prater 255
 
3.5%
m.bryant 250
 
3.4%
s.gostkowski 247
 
3.4%
n.folk 231
 
3.1%
r.succop 221
 
3.0%
Other values (91) 4649
63.3%
2024-08-08T20:46:26.628595image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 7341
 
11.8%
a 4387
 
7.0%
o 4155
 
6.7%
r 3373
 
5.4%
s 3158
 
5.1%
i 3090
 
5.0%
n 3047
 
4.9%
e 2847
 
4.6%
k 2422
 
3.9%
t 2389
 
3.8%
Other values (37) 26201
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 7341
 
11.8%
a 4387
 
7.0%
o 4155
 
6.7%
r 3373
 
5.4%
s 3158
 
5.1%
i 3090
 
5.0%
n 3047
 
4.9%
e 2847
 
4.6%
k 2422
 
3.9%
t 2389
 
3.8%
Other values (37) 26201
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 7341
 
11.8%
a 4387
 
7.0%
o 4155
 
6.7%
r 3373
 
5.4%
s 3158
 
5.1%
i 3090
 
5.0%
n 3047
 
4.9%
e 2847
 
4.6%
k 2422
 
3.9%
t 2389
 
3.8%
Other values (37) 26201
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 7341
 
11.8%
a 4387
 
7.0%
o 4155
 
6.7%
r 3373
 
5.4%
s 3158
 
5.1%
i 3090
 
5.0%
n 3047
 
4.9%
e 2847
 
4.6%
k 2422
 
3.9%
t 2389
 
3.8%
Other values (37) 26201
42.0%
Distinct202
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size104.0 KiB
2024-08-08T20:46:26.876833image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length20
Median length18
Mean length12.261547
Min length7

Characters and Unicode

Total characters163005
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)0.2%

Sample

1st rowDavid Akers
2nd rowDavid Akers
3rd rowDavid Akers
4th rowDavid Akers
5th rowDavid Akers
ValueCountFrequency (%)
matt 784
 
2.9%
jason 625
 
2.3%
josh 461
 
1.7%
john 459
 
1.7%
ryan 443
 
1.7%
stephen 418
 
1.6%
brown 388
 
1.5%
graham 384
 
1.4%
mike 365
 
1.4%
nick 356
 
1.3%
Other values (318) 21942
82.4%
2024-08-08T20:46:27.247190image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 15149
 
9.3%
13331
 
8.2%
e 12820
 
7.9%
n 12342
 
7.6%
o 10151
 
6.2%
i 9676
 
5.9%
r 9498
 
5.8%
s 7793
 
4.8%
t 6984
 
4.3%
l 6283
 
3.9%
Other values (42) 58978
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 163005
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 15149
 
9.3%
13331
 
8.2%
e 12820
 
7.9%
n 12342
 
7.6%
o 10151
 
6.2%
i 9676
 
5.9%
r 9498
 
5.8%
s 7793
 
4.8%
t 6984
 
4.3%
l 6283
 
3.9%
Other values (42) 58978
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 163005
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 15149
 
9.3%
13331
 
8.2%
e 12820
 
7.9%
n 12342
 
7.6%
o 10151
 
6.2%
i 9676
 
5.9%
r 9498
 
5.8%
s 7793
 
4.8%
t 6984
 
4.3%
l 6283
 
3.9%
Other values (42) 58978
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 163005
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 15149
 
9.3%
13331
 
8.2%
e 12820
 
7.9%
n 12342
 
7.6%
o 10151
 
6.2%
i 9676
 
5.9%
r 9498
 
5.8%
s 7793
 
4.8%
t 6984
 
4.3%
l 6283
 
3.9%
Other values (42) 58978
36.2%

position
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size104.0 KiB
K
13243 
P
 
41
WR
 
2
LB
 
2
SS
 
2
Other values (4)
 
4

Length

Max length2
Median length1
Mean length1.000677
Min length1

Characters and Unicode

Total characters13303
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowK
2nd rowK
3rd rowK
4th rowK
5th rowK

Common Values

ValueCountFrequency (%)
K 13243
99.6%
P 41
 
0.3%
WR 2
 
< 0.1%
LB 2
 
< 0.1%
SS 2
 
< 0.1%
C 1
 
< 0.1%
QB 1
 
< 0.1%
DE 1
 
< 0.1%
RB 1
 
< 0.1%

Length

2024-08-08T20:46:27.361297image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:27.464395image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
k 13243
99.6%
p 41
 
0.3%
wr 2
 
< 0.1%
lb 2
 
< 0.1%
ss 2
 
< 0.1%
c 1
 
< 0.1%
qb 1
 
< 0.1%
de 1
 
< 0.1%
rb 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
K 13243
99.5%
P 41
 
0.3%
B 4
 
< 0.1%
S 4
 
< 0.1%
R 3
 
< 0.1%
W 2
 
< 0.1%
L 2
 
< 0.1%
C 1
 
< 0.1%
Q 1
 
< 0.1%
D 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13303
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
K 13243
99.5%
P 41
 
0.3%
B 4
 
< 0.1%
S 4
 
< 0.1%
R 3
 
< 0.1%
W 2
 
< 0.1%
L 2
 
< 0.1%
C 1
 
< 0.1%
Q 1
 
< 0.1%
D 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13303
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
K 13243
99.5%
P 41
 
0.3%
B 4
 
< 0.1%
S 4
 
< 0.1%
R 3
 
< 0.1%
W 2
 
< 0.1%
L 2
 
< 0.1%
C 1
 
< 0.1%
Q 1
 
< 0.1%
D 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13303
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
K 13243
99.5%
P 41
 
0.3%
B 4
 
< 0.1%
S 4
 
< 0.1%
R 3
 
< 0.1%
W 2
 
< 0.1%
L 2
 
< 0.1%
C 1
 
< 0.1%
Q 1
 
< 0.1%
D 1
 
< 0.1%

position_group
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size104.0 KiB
SPEC
13284 
WR
 
2
LB
 
2
DB
 
2
OL
 
1
Other values (3)
 
3

Length

Max length4
Median length4
Mean length3.9984956
Min length2

Characters and Unicode

Total characters53156
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowSPEC
2nd rowSPEC
3rd rowSPEC
4th rowSPEC
5th rowSPEC

Common Values

ValueCountFrequency (%)
SPEC 13284
99.9%
WR 2
 
< 0.1%
LB 2
 
< 0.1%
DB 2
 
< 0.1%
OL 1
 
< 0.1%
QB 1
 
< 0.1%
DL 1
 
< 0.1%
RB 1
 
< 0.1%

Length

2024-08-08T20:46:27.590514image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:27.698616image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
spec 13284
99.9%
wr 2
 
< 0.1%
lb 2
 
< 0.1%
db 2
 
< 0.1%
ol 1
 
< 0.1%
qb 1
 
< 0.1%
dl 1
 
< 0.1%
rb 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 13284
25.0%
P 13284
25.0%
E 13284
25.0%
C 13284
25.0%
B 6
 
< 0.1%
L 4
 
< 0.1%
R 3
 
< 0.1%
D 3
 
< 0.1%
W 2
 
< 0.1%
O 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53156
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 13284
25.0%
P 13284
25.0%
E 13284
25.0%
C 13284
25.0%
B 6
 
< 0.1%
L 4
 
< 0.1%
R 3
 
< 0.1%
D 3
 
< 0.1%
W 2
 
< 0.1%
O 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53156
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 13284
25.0%
P 13284
25.0%
E 13284
25.0%
C 13284
25.0%
B 6
 
< 0.1%
L 4
 
< 0.1%
R 3
 
< 0.1%
D 3
 
< 0.1%
W 2
 
< 0.1%
O 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53156
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 13284
25.0%
P 13284
25.0%
E 13284
25.0%
C 13284
25.0%
B 6
 
< 0.1%
L 4
 
< 0.1%
R 3
 
< 0.1%
D 3
 
< 0.1%
W 2
 
< 0.1%
O 1
 
< 0.1%

headshot_url
Text

MISSING 

Distinct105
Distinct (%)1.3%
Missing5364
Missing (%)40.3%
Memory size104.0 KiB
2024-08-08T20:46:27.891799image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length82
Median length82
Mean length81.940858
Min length81

Characters and Unicode

Total characters649791
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)0.2%

Sample

1st rowhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/dypn7kf7fsaz30ey0ayc
2nd rowhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/dypn7kf7fsaz30ey0ayc
3rd rowhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/dypn7kf7fsaz30ey0ayc
4th rowhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/dypn7kf7fsaz30ey0ayc
5th rowhttps://static.www.nfl.com/image/private/f_auto,q_auto/league/dypn7kf7fsaz30ey0ayc
ValueCountFrequency (%)
https://static.www.nfl.com/image/private/f_auto,q_auto/league/imfj1hl4kob4jof8hcwa 341
 
4.3%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/dypn7kf7fsaz30ey0ayc 301
 
3.8%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/c5zvm3ux5uvwxdfqwd0w 286
 
3.6%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/uakpiwhnutg2rvoyrmgh 281
 
3.5%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/usbwgcjsdcugjafgjzvt 279
 
3.5%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/fqsjhn7vjb6lzhzctyiq 255
 
3.2%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/okrutwlsyxd3w2xtohun 250
 
3.2%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/nqi0psnge6kkglcspabo 247
 
3.1%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/uwaf16pn60rzz9wedlzt 231
 
2.9%
https://static.www.nfl.com/image/private/f_auto,q_auto/league/sbdymbap2wadw2jbey6w 221
 
2.8%
Other values (95) 5238
66.1%
2024-08-08T20:46:28.188097image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 59086
 
9.1%
/ 55510
 
8.5%
a 53653
 
8.3%
e 37124
 
5.7%
w 30278
 
4.7%
u 30122
 
4.6%
o 28445
 
4.4%
i 27566
 
4.2%
. 23790
 
3.7%
s 22151
 
3.4%
Other values (31) 282066
43.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 649791
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 59086
 
9.1%
/ 55510
 
8.5%
a 53653
 
8.3%
e 37124
 
5.7%
w 30278
 
4.7%
u 30122
 
4.6%
o 28445
 
4.4%
i 27566
 
4.2%
. 23790
 
3.7%
s 22151
 
3.4%
Other values (31) 282066
43.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 649791
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 59086
 
9.1%
/ 55510
 
8.5%
a 53653
 
8.3%
e 37124
 
5.7%
w 30278
 
4.7%
u 30122
 
4.6%
o 28445
 
4.4%
i 27566
 
4.2%
. 23790
 
3.7%
s 22151
 
3.4%
Other values (31) 282066
43.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 649791
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 59086
 
9.1%
/ 55510
 
8.5%
a 53653
 
8.3%
e 37124
 
5.7%
w 30278
 
4.7%
u 30122
 
4.6%
o 28445
 
4.4%
i 27566
 
4.2%
. 23790
 
3.7%
s 22151
 
3.4%
Other values (31) 282066
43.4%

fg_made
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)0.1%
Missing1640
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean1.8053029
Minimum0
Maximum8
Zeros853
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size104.0 KiB
2024-08-08T20:46:28.284189image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0944118
Coefficient of variation (CV)0.60622061
Kurtosis0.54219077
Mean1.8053029
Median Absolute Deviation (MAD)1
Skewness0.71143713
Sum21039
Variance1.1977373
MonotonicityNot monotonic
2024-08-08T20:46:28.379282image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 4425
33.3%
2 3601
27.1%
3 1905
14.3%
0 853
 
6.4%
4 690
 
5.2%
5 151
 
1.1%
6 22
 
0.2%
7 6
 
< 0.1%
8 1
 
< 0.1%
(Missing) 1640
 
12.3%
ValueCountFrequency (%)
0 853
 
6.4%
1 4425
33.3%
2 3601
27.1%
3 1905
14.3%
4 690
 
5.2%
5 151
 
1.1%
6 22
 
0.2%
7 6
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 6
 
< 0.1%
6 22
 
0.2%
5 151
 
1.1%
4 690
 
5.2%
3 1905
14.3%
2 3601
27.1%
1 4425
33.3%
0 853
 
6.4%

fg_att
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9162028
Minimum0
Maximum8
Zeros1640
Zeros (%)12.3%
Negative0
Negative (%)0.0%
Memory size104.0 KiB
2024-08-08T20:46:28.475378image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2702479
Coefficient of variation (CV)0.66289846
Kurtosis0.11854758
Mean1.9162028
Median Absolute Deviation (MAD)1
Skewness0.53881397
Sum25474
Variance1.6135297
MonotonicityNot monotonic
2024-08-08T20:46:28.570827image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 3900
29.3%
1 3793
28.5%
3 2464
18.5%
0 1640
12.3%
4 1092
 
8.2%
5 325
 
2.4%
6 68
 
0.5%
7 8
 
0.1%
8 4
 
< 0.1%
ValueCountFrequency (%)
0 1640
12.3%
1 3793
28.5%
2 3900
29.3%
3 2464
18.5%
4 1092
 
8.2%
5 325
 
2.4%
6 68
 
0.5%
7 8
 
0.1%
8 4
 
< 0.1%
ValueCountFrequency (%)
8 4
 
< 0.1%
7 8
 
0.1%
6 68
 
0.5%
5 325
 
2.4%
4 1092
 
8.2%
3 2464
18.5%
2 3900
29.3%
1 3793
28.5%
0 1640
12.3%

fg_missed
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing1640
Missing (%)12.3%
Memory size104.0 KiB
0.0
8255 
1.0
2961 
2.0
 
393
3.0
 
42
4.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters34962
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 8255
62.1%
1.0 2961
 
22.3%
2.0 393
 
3.0%
3.0 42
 
0.3%
4.0 3
 
< 0.1%
(Missing) 1640
 
12.3%

Length

2024-08-08T20:46:28.675930image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:28.765256image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 8255
70.8%
1.0 2961
 
25.4%
2.0 393
 
3.4%
3.0 42
 
0.4%
4.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 19909
56.9%
. 11654
33.3%
1 2961
 
8.5%
2 393
 
1.1%
3 42
 
0.1%
4 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19909
56.9%
. 11654
33.3%
1 2961
 
8.5%
2 393
 
1.1%
3 42
 
0.1%
4 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19909
56.9%
. 11654
33.3%
1 2961
 
8.5%
2 393
 
1.1%
3 42
 
0.1%
4 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19909
56.9%
. 11654
33.3%
1 2961
 
8.5%
2 393
 
1.1%
3 42
 
0.1%
4 3
 
< 0.1%

fg_blocked
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing1640
Missing (%)12.3%
Memory size104.0 KiB
0.0
11122 
1.0
 
514
2.0
 
18

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters34962
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11122
83.7%
1.0 514
 
3.9%
2.0 18
 
0.1%
(Missing) 1640
 
12.3%

Length

2024-08-08T20:46:28.864061image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:28.947433image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11122
95.4%
1.0 514
 
4.4%
2.0 18
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 22776
65.1%
. 11654
33.3%
1 514
 
1.5%
2 18
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 22776
65.1%
. 11654
33.3%
1 514
 
1.5%
2 18
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 22776
65.1%
. 11654
33.3%
1 514
 
1.5%
2 18
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 22776
65.1%
. 11654
33.3%
1 514
 
1.5%
2 18
 
0.1%

fg_long
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct48
Distinct (%)0.4%
Missing2493
Missing (%)18.8%
Infinite0
Infinite (%)0.0%
Mean39.793075
Minimum18
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.0 KiB
2024-08-08T20:46:29.044528image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile23
Q133
median41
Q347
95-th percentile54
Maximum66
Range48
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.4819542
Coefficient of variation (CV)0.23828152
Kurtosis-0.78690785
Mean39.793075
Median Absolute Deviation (MAD)7
Skewness-0.26921704
Sum429805
Variance89.907456
MonotonicityNot monotonic
2024-08-08T20:46:29.164639image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
48 461
 
3.5%
43 445
 
3.3%
47 426
 
3.2%
38 411
 
3.1%
40 408
 
3.1%
42 396
 
3.0%
46 393
 
3.0%
39 387
 
2.9%
41 386
 
2.9%
45 384
 
2.9%
Other values (38) 6704
50.4%
(Missing) 2493
 
18.8%
ValueCountFrequency (%)
18 8
 
0.1%
19 67
 
0.5%
20 117
0.9%
21 118
0.9%
22 151
1.1%
23 198
1.5%
24 177
1.3%
25 173
1.3%
26 182
1.4%
27 209
1.6%
ValueCountFrequency (%)
66 1
 
< 0.1%
64 1
 
< 0.1%
63 4
 
< 0.1%
62 7
 
0.1%
61 11
 
0.1%
60 9
 
0.1%
59 18
 
0.1%
58 28
 
0.2%
57 52
0.4%
56 80
0.6%

fg_pct
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct16
Distinct (%)0.1%
Missing1640
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean0.82256478
Minimum0
Maximum1
Zeros853
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size104.0 KiB
2024-08-08T20:46:29.266734image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.667
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.333

Descriptive statistics

Standard deviation0.29812552
Coefficient of variation (CV)0.36243409
Kurtosis1.6331089
Mean0.82256478
Median Absolute Deviation (MAD)0
Skewness-1.6342924
Sum9586.17
Variance0.088878826
MonotonicityNot monotonic
2024-08-08T20:46:29.364540image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 7853
59.1%
0.5 1264
 
9.5%
0 853
 
6.4%
0.667 832
 
6.3%
0.75 407
 
3.1%
0.333 194
 
1.5%
0.8 137
 
1.0%
0.6 57
 
0.4%
0.833 25
 
0.2%
0.25 20
 
0.2%
Other values (6) 12
 
0.1%
(Missing) 1640
 
12.3%
ValueCountFrequency (%)
0 853
6.4%
0.2 2
 
< 0.1%
0.25 20
 
0.2%
0.333 194
 
1.5%
0.4 4
 
< 0.1%
0.5 1264
9.5%
0.571 1
 
< 0.1%
0.6 57
 
0.4%
0.667 832
6.3%
0.714 1
 
< 0.1%
ValueCountFrequency (%)
1 7853
59.1%
0.875 2
 
< 0.1%
0.857 2
 
< 0.1%
0.833 25
 
0.2%
0.8 137
 
1.0%
0.75 407
 
3.1%
0.714 1
 
< 0.1%
0.667 832
 
6.3%
0.6 57
 
0.4%
0.571 1
 
< 0.1%

fg_made_0_19
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing1640
Missing (%)12.3%
Memory size104.0 KiB
0.0
11363 
1.0
 
284
2.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters34962
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11363
85.5%
1.0 284
 
2.1%
2.0 7
 
0.1%
(Missing) 1640
 
12.3%

Length

2024-08-08T20:46:29.464631image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:29.734887image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11363
97.5%
1.0 284
 
2.4%
2.0 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 23017
65.8%
. 11654
33.3%
1 284
 
0.8%
2 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23017
65.8%
. 11654
33.3%
1 284
 
0.8%
2 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23017
65.8%
. 11654
33.3%
1 284
 
0.8%
2 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23017
65.8%
. 11654
33.3%
1 284
 
0.8%
2 7
 
< 0.1%

fg_made_20_29
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)0.1%
Missing1640
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean0.54444826
Minimum0
Maximum5
Zeros6539
Zeros (%)49.2%
Negative0
Negative (%)0.0%
Memory size104.0 KiB
2024-08-08T20:46:29.812383image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.69806685
Coefficient of variation (CV)1.2821546
Kurtosis1.1448187
Mean0.54444826
Median Absolute Deviation (MAD)0
Skewness1.1660258
Sum6345
Variance0.48729733
MonotonicityNot monotonic
2024-08-08T20:46:29.903459image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 6539
49.2%
1 4037
30.4%
2 936
 
7.0%
3 134
 
1.0%
4 6
 
< 0.1%
5 2
 
< 0.1%
(Missing) 1640
 
12.3%
ValueCountFrequency (%)
0 6539
49.2%
1 4037
30.4%
2 936
 
7.0%
3 134
 
1.0%
4 6
 
< 0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
4 6
 
< 0.1%
3 134
 
1.0%
2 936
 
7.0%
1 4037
30.4%
0 6539
49.2%

fg_made_30_39
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing1640
Missing (%)12.3%
Memory size104.0 KiB
0.0
6387 
1.0
4094 
2.0
1026 
3.0
 
127
4.0
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters34962
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6387
48.0%
1.0 4094
30.8%
2.0 1026
 
7.7%
3.0 127
 
1.0%
4.0 20
 
0.2%
(Missing) 1640
 
12.3%

Length

2024-08-08T20:46:30.002551image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:30.091632image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6387
54.8%
1.0 4094
35.1%
2.0 1026
 
8.8%
3.0 127
 
1.1%
4.0 20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 18041
51.6%
. 11654
33.3%
1 4094
 
11.7%
2 1026
 
2.9%
3 127
 
0.4%
4 20
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18041
51.6%
. 11654
33.3%
1 4094
 
11.7%
2 1026
 
2.9%
3 127
 
0.4%
4 20
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18041
51.6%
. 11654
33.3%
1 4094
 
11.7%
2 1026
 
2.9%
3 127
 
0.4%
4 20
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18041
51.6%
. 11654
33.3%
1 4094
 
11.7%
2 1026
 
2.9%
3 127
 
0.4%
4 20
 
0.1%

fg_made_40_49
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)0.1%
Missing1640
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean0.49356444
Minimum0
Maximum5
Zeros6988
Zeros (%)52.6%
Negative0
Negative (%)0.0%
Memory size104.0 KiB
2024-08-08T20:46:30.181719image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.68054686
Coefficient of variation (CV)1.3788409
Kurtosis1.6607272
Mean0.49356444
Median Absolute Deviation (MAD)0
Skewness1.3205514
Sum5752
Variance0.46314403
MonotonicityNot monotonic
2024-08-08T20:46:30.272804image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 6988
52.6%
1 3720
28.0%
2 821
 
6.2%
3 111
 
0.8%
4 13
 
0.1%
5 1
 
< 0.1%
(Missing) 1640
 
12.3%
ValueCountFrequency (%)
0 6988
52.6%
1 3720
28.0%
2 821
 
6.2%
3 111
 
0.8%
4 13
 
0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 13
 
0.1%
3 111
 
0.8%
2 821
 
6.2%
1 3720
28.0%
0 6988
52.6%

fg_made_50_59
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing1640
Missing (%)12.3%
Memory size104.0 KiB
0.0
9847 
1.0
1628 
2.0
 
163
3.0
 
14
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters34962
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 9847
74.1%
1.0 1628
 
12.2%
2.0 163
 
1.2%
3.0 14
 
0.1%
4.0 2
 
< 0.1%
(Missing) 1640
 
12.3%

Length

2024-08-08T20:46:30.373899image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:30.461983image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 9847
84.5%
1.0 1628
 
14.0%
2.0 163
 
1.4%
3.0 14
 
0.1%
4.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 21501
61.5%
. 11654
33.3%
1 1628
 
4.7%
2 163
 
0.5%
3 14
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21501
61.5%
. 11654
33.3%
1 1628
 
4.7%
2 163
 
0.5%
3 14
 
< 0.1%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21501
61.5%
. 11654
33.3%
1 1628
 
4.7%
2 163
 
0.5%
3 14
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21501
61.5%
. 11654
33.3%
1 1628
 
4.7%
2 163
 
0.5%
3 14
 
< 0.1%
4 2
 
< 0.1%

fg_made_60_
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1640
Missing (%)12.3%
Memory size104.0 KiB
0.0
11621 
1.0
 
33

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters34962
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11621
87.4%
1.0 33
 
0.2%
(Missing) 1640
 
12.3%

Length

2024-08-08T20:46:30.562079image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:30.642155image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11621
99.7%
1.0 33
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 23275
66.6%
. 11654
33.3%
1 33
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23275
66.6%
. 11654
33.3%
1 33
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23275
66.6%
. 11654
33.3%
1 33
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23275
66.6%
. 11654
33.3%
1 33
 
0.1%

fg_missed_0_19
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing1640
Missing (%)12.3%
Memory size104.0 KiB
0.0
11654 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters34962
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11654
87.7%
(Missing) 1640
 
12.3%

Length

2024-08-08T20:46:30.729236image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:30.808311image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11654
100.0%

Most occurring characters

ValueCountFrequency (%)
0 23308
66.7%
. 11654
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23308
66.7%
. 11654
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23308
66.7%
. 11654
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23308
66.7%
. 11654
33.3%

fg_missed_20_29
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing1640
Missing (%)12.3%
Memory size104.0 KiB
0.0
11490 
1.0
 
163
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters34962
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11490
86.4%
1.0 163
 
1.2%
2.0 1
 
< 0.1%
(Missing) 1640
 
12.3%

Length

2024-08-08T20:46:30.891388image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:30.974825image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11490
98.6%
1.0 163
 
1.4%
2.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 23144
66.2%
. 11654
33.3%
1 163
 
0.5%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23144
66.2%
. 11654
33.3%
1 163
 
0.5%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23144
66.2%
. 11654
33.3%
1 163
 
0.5%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23144
66.2%
. 11654
33.3%
1 163
 
0.5%
2 1
 
< 0.1%

fg_missed_30_39
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing1640
Missing (%)12.3%
Memory size104.0 KiB
0.0
10943 
1.0
 
689
2.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters34962
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 10943
82.3%
1.0 689
 
5.2%
2.0 22
 
0.2%
(Missing) 1640
 
12.3%

Length

2024-08-08T20:46:31.064238image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:31.145799image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 10943
93.9%
1.0 689
 
5.9%
2.0 22
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 22597
64.6%
. 11654
33.3%
1 689
 
2.0%
2 22
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 22597
64.6%
. 11654
33.3%
1 689
 
2.0%
2 22
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 22597
64.6%
. 11654
33.3%
1 689
 
2.0%
2 22
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 22597
64.6%
. 11654
33.3%
1 689
 
2.0%
2 22
 
0.1%

fg_missed_40_49
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing1640
Missing (%)12.3%
Memory size104.0 KiB
0.0
9983 
1.0
1568 
2.0
 
101
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters34962
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 9983
75.1%
1.0 1568
 
11.8%
2.0 101
 
0.8%
3.0 2
 
< 0.1%
(Missing) 1640
 
12.3%

Length

2024-08-08T20:46:31.238151image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:31.323293image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 9983
85.7%
1.0 1568
 
13.5%
2.0 101
 
0.9%
3.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 21637
61.9%
. 11654
33.3%
1 1568
 
4.5%
2 101
 
0.3%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21637
61.9%
. 11654
33.3%
1 1568
 
4.5%
2 101
 
0.3%
3 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21637
61.9%
. 11654
33.3%
1 1568
 
4.5%
2 101
 
0.3%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21637
61.9%
. 11654
33.3%
1 1568
 
4.5%
2 101
 
0.3%
3 2
 
< 0.1%

fg_missed_50_59
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing1640
Missing (%)12.3%
Memory size104.0 KiB
0.0
10557 
1.0
1063 
2.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters34962
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 10557
79.4%
1.0 1063
 
8.0%
2.0 34
 
0.3%
(Missing) 1640
 
12.3%

Length

2024-08-08T20:46:31.419385image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:31.501461image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 10557
90.6%
1.0 1063
 
9.1%
2.0 34
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 22211
63.5%
. 11654
33.3%
1 1063
 
3.0%
2 34
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 22211
63.5%
. 11654
33.3%
1 1063
 
3.0%
2 34
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 22211
63.5%
. 11654
33.3%
1 1063
 
3.0%
2 34
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 22211
63.5%
. 11654
33.3%
1 1063
 
3.0%
2 34
 
0.1%

fg_missed_60_
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1640
Missing (%)12.3%
Memory size104.0 KiB
0.0
11574 
1.0
 
80

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters34962
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11574
87.1%
1.0 80
 
0.6%
(Missing) 1640
 
12.3%

Length

2024-08-08T20:46:31.595550image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:31.675626image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11574
99.3%
1.0 80
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 23228
66.4%
. 11654
33.3%
1 80
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23228
66.4%
. 11654
33.3%
1 80
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23228
66.4%
. 11654
33.3%
1 80
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23228
66.4%
. 11654
33.3%
1 80
 
0.2%

fg_made_list
Text

MISSING 

Distinct4037
Distinct (%)37.4%
Missing2493
Missing (%)18.8%
Memory size104.0 KiB
2024-08-08T20:46:31.944233image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length23
Median length20
Mean length4.8436256
Min length2

Characters and Unicode

Total characters52316
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3035 ?
Unique (%)28.1%

Sample

1st row53
2nd row48
3rd row46
4th row41;35
5th row36;44
ValueCountFrequency (%)
38 163
 
1.5%
23 158
 
1.5%
28 158
 
1.5%
33 155
 
1.4%
40 149
 
1.4%
31 149
 
1.4%
34 147
 
1.4%
27 145
 
1.3%
43 144
 
1.3%
24 141
 
1.3%
Other values (4027) 9292
86.0%
2024-08-08T20:46:32.324114image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
; 10238
19.6%
3 9163
17.5%
2 8582
16.4%
4 7718
14.8%
5 3955
 
7.6%
1 2489
 
4.8%
0 2239
 
4.3%
8 2125
 
4.1%
9 2020
 
3.9%
7 1913
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52316
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
; 10238
19.6%
3 9163
17.5%
2 8582
16.4%
4 7718
14.8%
5 3955
 
7.6%
1 2489
 
4.8%
0 2239
 
4.3%
8 2125
 
4.1%
9 2020
 
3.9%
7 1913
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52316
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
; 10238
19.6%
3 9163
17.5%
2 8582
16.4%
4 7718
14.8%
5 3955
 
7.6%
1 2489
 
4.8%
0 2239
 
4.3%
8 2125
 
4.1%
9 2020
 
3.9%
7 1913
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52316
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
; 10238
19.6%
3 9163
17.5%
2 8582
16.4%
4 7718
14.8%
5 3955
 
7.6%
1 2489
 
4.8%
0 2239
 
4.3%
8 2125
 
4.1%
9 2020
 
3.9%
7 1913
 
3.7%

fg_missed_list
Text

MISSING 

Distinct399
Distinct (%)11.7%
Missing9895
Missing (%)74.4%
Memory size104.0 KiB
2024-08-08T20:46:32.534856image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length11
Median length2
Mean length2.4289497
Min length2

Characters and Unicode

Total characters8256
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique283 ?
Unique (%)8.3%

Sample

1st row59
2nd row51
3rd row35;39
4th row49
5th row33
ValueCountFrequency (%)
48 215
 
6.3%
52 158
 
4.6%
46 150
 
4.4%
47 148
 
4.4%
50 140
 
4.1%
53 138
 
4.1%
51 134
 
3.9%
43 132
 
3.9%
49 131
 
3.9%
44 128
 
3.8%
Other values (389) 1925
56.6%
2024-08-08T20:46:32.830531image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 2167
26.2%
5 1478
17.9%
3 1184
14.3%
2 588
 
7.1%
; 486
 
5.9%
8 469
 
5.7%
6 419
 
5.1%
1 373
 
4.5%
7 367
 
4.4%
0 363
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 2167
26.2%
5 1478
17.9%
3 1184
14.3%
2 588
 
7.1%
; 486
 
5.9%
8 469
 
5.7%
6 419
 
5.1%
1 373
 
4.5%
7 367
 
4.4%
0 363
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 2167
26.2%
5 1478
17.9%
3 1184
14.3%
2 588
 
7.1%
; 486
 
5.9%
8 469
 
5.7%
6 419
 
5.1%
1 373
 
4.5%
7 367
 
4.4%
0 363
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 2167
26.2%
5 1478
17.9%
3 1184
14.3%
2 588
 
7.1%
; 486
 
5.9%
8 469
 
5.7%
6 419
 
5.1%
1 373
 
4.5%
7 367
 
4.4%
0 363
 
4.4%

fg_blocked_list
Text

MISSING 

Distinct103
Distinct (%)19.4%
Missing12762
Missing (%)96.0%
Memory size104.0 KiB
2024-08-08T20:46:33.024235image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.3684211
Min length2

Characters and Unicode

Total characters1792
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)6.4%

Sample

1st row50.0
2nd row26.0
3rd row42.0
4th row37.0
5th row49.0
ValueCountFrequency (%)
48.0 21
 
3.9%
43.0 19
 
3.6%
46.0 16
 
3.0%
38.0 15
 
2.8%
44.0 14
 
2.6%
50.0 14
 
2.6%
51.0 13
 
2.4%
53.0 13
 
2.4%
49.0 13
 
2.4%
48 12
 
2.3%
Other values (93) 382
71.8%
2024-08-08T20:46:33.336004image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 397
22.2%
. 337
18.8%
4 273
15.2%
3 210
11.7%
5 144
 
8.0%
2 116
 
6.5%
8 73
 
4.1%
6 61
 
3.4%
1 56
 
3.1%
7 56
 
3.1%
Other values (2) 69
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1792
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 397
22.2%
. 337
18.8%
4 273
15.2%
3 210
11.7%
5 144
 
8.0%
2 116
 
6.5%
8 73
 
4.1%
6 61
 
3.4%
1 56
 
3.1%
7 56
 
3.1%
Other values (2) 69
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1792
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 397
22.2%
. 337
18.8%
4 273
15.2%
3 210
11.7%
5 144
 
8.0%
2 116
 
6.5%
8 73
 
4.1%
6 61
 
3.4%
1 56
 
3.1%
7 56
 
3.1%
Other values (2) 69
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1792
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 397
22.2%
. 337
18.8%
4 273
15.2%
3 210
11.7%
5 144
 
8.0%
2 116
 
6.5%
8 73
 
4.1%
6 61
 
3.4%
1 56
 
3.1%
7 56
 
3.1%
Other values (2) 69
 
3.9%

fg_made_distance
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct221
Distinct (%)1.9%
Missing1640
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean64.520251
Minimum0
Maximum274
Zeros853
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size104.0 KiB
2024-08-08T20:46:33.448040image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q134
median57
Q388
95-th percentile143
Maximum274
Range274
Interquartile range (IQR)54

Descriptive statistics

Standard deviation41.773593
Coefficient of variation (CV)0.64744934
Kurtosis0.89181584
Mean64.520251
Median Absolute Deviation (MAD)26
Skewness0.8492536
Sum751919
Variance1745.0331
MonotonicityNot monotonic
2024-08-08T20:46:33.565462image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 853
 
6.4%
43 163
 
1.2%
38 163
 
1.2%
28 158
 
1.2%
23 158
 
1.2%
33 155
 
1.2%
48 154
 
1.2%
40 152
 
1.1%
31 149
 
1.1%
44 147
 
1.1%
Other values (211) 9402
70.7%
(Missing) 1640
 
12.3%
ValueCountFrequency (%)
0 853
6.4%
18 8
 
0.1%
19 67
 
0.5%
20 111
 
0.8%
21 109
 
0.8%
22 133
 
1.0%
23 158
 
1.2%
24 141
 
1.1%
25 128
 
1.0%
26 129
 
1.0%
ValueCountFrequency (%)
274 1
< 0.1%
271 1
< 0.1%
267 1
< 0.1%
262 2
< 0.1%
260 1
< 0.1%
257 2
< 0.1%
255 1
< 0.1%
253 1
< 0.1%
250 1
< 0.1%
248 1
< 0.1%

fg_missed_distance
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct125
Distinct (%)1.1%
Missing1640
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean15.03192
Minimum0
Maximum210
Zeros8255
Zeros (%)62.1%
Negative0
Negative (%)0.0%
Memory size104.0 KiB
2024-08-08T20:46:33.688574image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q337
95-th percentile57
Maximum210
Range210
Interquartile range (IQR)37

Descriptive statistics

Standard deviation25.596899
Coefficient of variation (CV)1.7028363
Kurtosis2.6134197
Mean15.03192
Median Absolute Deviation (MAD)0
Skewness1.6603907
Sum175182
Variance655.20125
MonotonicityNot monotonic
2024-08-08T20:46:33.805683image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8255
62.1%
48 215
 
1.6%
52 159
 
1.2%
46 150
 
1.1%
47 148
 
1.1%
50 140
 
1.1%
53 138
 
1.0%
51 134
 
1.0%
43 132
 
1.0%
49 131
 
1.0%
Other values (115) 2052
 
15.4%
(Missing) 1640
 
12.3%
ValueCountFrequency (%)
0 8255
62.1%
20 1
 
< 0.1%
21 4
 
< 0.1%
22 6
 
< 0.1%
23 9
 
0.1%
24 13
 
0.1%
25 5
 
< 0.1%
26 13
 
0.1%
27 13
 
0.1%
28 24
 
0.2%
ValueCountFrequency (%)
210 1
< 0.1%
177 1
< 0.1%
157 1
< 0.1%
155 1
< 0.1%
149 1
< 0.1%
147 1
< 0.1%
146 1
< 0.1%
145 1
< 0.1%
143 1
< 0.1%
142 1
< 0.1%

fg_blocked_distance
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct64
Distinct (%)0.5%
Missing1640
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean1.9557234
Minimum0
Maximum133
Zeros11122
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size104.0 KiB
2024-08-08T20:46:33.924833image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum133
Range133
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.3138363
Coefficient of variation (CV)4.7623485
Kurtosis27.332813
Mean1.9557234
Median Absolute Deviation (MAD)0
Skewness5.0472648
Sum22792
Variance86.747546
MonotonicityNot monotonic
2024-08-08T20:46:34.053953image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11122
83.7%
48 33
 
0.2%
49 24
 
0.2%
43 24
 
0.2%
37 22
 
0.2%
38 21
 
0.2%
46 20
 
0.2%
44 20
 
0.2%
47 19
 
0.1%
50 19
 
0.1%
Other values (54) 330
 
2.5%
(Missing) 1640
 
12.3%
ValueCountFrequency (%)
0 11122
83.7%
19 1
 
< 0.1%
20 4
 
< 0.1%
21 3
 
< 0.1%
22 6
 
< 0.1%
23 3
 
< 0.1%
24 5
 
< 0.1%
25 5
 
< 0.1%
26 7
 
0.1%
27 9
 
0.1%
ValueCountFrequency (%)
133 1
< 0.1%
99 1
< 0.1%
98 1
< 0.1%
94 1
< 0.1%
92 1
< 0.1%
88 1
< 0.1%
85 1
< 0.1%
84 1
< 0.1%
82 1
< 0.1%
76 1
< 0.1%

pat_made
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)0.1%
Missing1167
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean2.4738188
Minimum0
Maximum10
Zeros144
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size104.0 KiB
2024-08-08T20:46:34.160050image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3398151
Coefficient of variation (CV)0.54159793
Kurtosis0.4702945
Mean2.4738188
Median Absolute Deviation (MAD)1
Skewness0.7867938
Sum30000
Variance1.7951045
MonotonicityNot monotonic
2024-08-08T20:46:34.260145image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 3608
27.1%
1 3128
23.5%
3 2702
20.3%
4 1585
11.9%
5 643
 
4.8%
6 244
 
1.8%
0 144
 
1.1%
7 56
 
0.4%
8 15
 
0.1%
9 1
 
< 0.1%
(Missing) 1167
 
8.8%
ValueCountFrequency (%)
0 144
 
1.1%
1 3128
23.5%
2 3608
27.1%
3 2702
20.3%
4 1585
11.9%
5 643
 
4.8%
6 244
 
1.8%
7 56
 
0.4%
8 15
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 1
 
< 0.1%
8 15
 
0.1%
7 56
 
0.4%
6 244
 
1.8%
5 643
 
4.8%
4 1585
11.9%
3 2702
20.3%
2 3608
27.1%
1 3128
23.5%

pat_att
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.322025
Minimum0
Maximum10
Zeros1167
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size104.0 KiB
2024-08-08T20:46:34.357313image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4637322
Coefficient of variation (CV)0.63036884
Kurtosis0.18685308
Mean2.322025
Median Absolute Deviation (MAD)1
Skewness0.56914681
Sum30869
Variance2.1425119
MonotonicityNot monotonic
2024-08-08T20:46:34.455050image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 3622
27.2%
1 3036
22.8%
3 2774
20.9%
4 1658
12.5%
0 1167
 
8.8%
5 696
 
5.2%
6 252
 
1.9%
7 72
 
0.5%
8 15
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 1167
 
8.8%
1 3036
22.8%
2 3622
27.2%
3 2774
20.9%
4 1658
12.5%
5 696
 
5.2%
6 252
 
1.9%
7 72
 
0.5%
8 15
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 1
 
< 0.1%
8 15
 
0.1%
7 72
 
0.5%
6 252
 
1.9%
5 696
 
5.2%
4 1658
12.5%
3 2774
20.9%
2 3622
27.2%
1 3036
22.8%

pat_missed
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing1167
Missing (%)8.8%
Memory size104.0 KiB
0.0
11496 
1.0
 
586
2.0
 
44
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36381
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11496
86.5%
1.0 586
 
4.4%
2.0 44
 
0.3%
4.0 1
 
< 0.1%
(Missing) 1167
 
8.8%

Length

2024-08-08T20:46:34.559144image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:34.648954image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11496
94.8%
1.0 586
 
4.8%
2.0 44
 
0.4%
4.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 23623
64.9%
. 12127
33.3%
1 586
 
1.6%
2 44
 
0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36381
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23623
64.9%
. 12127
33.3%
1 586
 
1.6%
2 44
 
0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36381
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23623
64.9%
. 12127
33.3%
1 586
 
1.6%
2 44
 
0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36381
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23623
64.9%
. 12127
33.3%
1 586
 
1.6%
2 44
 
0.1%
4 1
 
< 0.1%

pat_blocked
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing1167
Missing (%)8.8%
Memory size104.0 KiB
0.0
11937 
1.0
 
189
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters36381
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11937
89.8%
1.0 189
 
1.4%
2.0 1
 
< 0.1%
(Missing) 1167
 
8.8%

Length

2024-08-08T20:46:34.745564image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:34.831843image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11937
98.4%
1.0 189
 
1.6%
2.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 24064
66.1%
. 12127
33.3%
1 189
 
0.5%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36381
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 24064
66.1%
. 12127
33.3%
1 189
 
0.5%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36381
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 24064
66.1%
. 12127
33.3%
1 189
 
0.5%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36381
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 24064
66.1%
. 12127
33.3%
1 189
 
0.5%
2 1
 
< 0.1%

pat_pct
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct13
Distinct (%)0.1%
Missing1167
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean0.96832976
Minimum0
Maximum1
Zeros144
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size104.0 KiB
2024-08-08T20:46:34.919727image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.13745377
Coefficient of variation (CV)0.14194934
Kurtosis29.939871
Mean0.96832976
Median Absolute Deviation (MAD)0
Skewness-5.2479854
Sum11742.935
Variance0.018893538
MonotonicityNot monotonic
2024-08-08T20:46:35.016825image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 11315
85.1%
0.5 213
 
1.6%
0.667 194
 
1.5%
0 144
 
1.1%
0.75 132
 
1.0%
0.8 66
 
0.5%
0.833 19
 
0.1%
0.333 19
 
0.1%
0.857 13
 
0.1%
0.6 7
 
0.1%
Other values (3) 5
 
< 0.1%
(Missing) 1167
 
8.8%
ValueCountFrequency (%)
0 144
1.1%
0.2 1
 
< 0.1%
0.333 19
 
0.1%
0.4 1
 
< 0.1%
0.5 213
1.6%
0.6 7
 
0.1%
0.667 194
1.5%
0.714 3
 
< 0.1%
0.75 132
1.0%
0.8 66
 
0.5%
ValueCountFrequency (%)
1 11315
85.1%
0.857 13
 
0.1%
0.833 19
 
0.1%
0.8 66
 
0.5%
0.75 132
 
1.0%
0.714 3
 
< 0.1%
0.667 194
 
1.5%
0.6 7
 
0.1%
0.5 213
 
1.6%
0.4 1
 
< 0.1%

gwfg_att
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.0 KiB
0
12071 
1
1221 
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13294
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12071
90.8%
1 1221
 
9.2%
2 2
 
< 0.1%

Length

2024-08-08T20:46:35.117767image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:35.204854image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 12071
90.8%
1 1221
 
9.2%
2 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 12071
90.8%
1 1221
 
9.2%
2 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13294
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12071
90.8%
1 1221
 
9.2%
2 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13294
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12071
90.8%
1 1221
 
9.2%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13294
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12071
90.8%
1 1221
 
9.2%
2 2
 
< 0.1%

gwfg_distance
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51
Distinct (%)4.2%
Missing12071
Missing (%)90.8%
Infinite0
Infinite (%)0.0%
Mean38.776778
Minimum18
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.0 KiB
2024-08-08T20:46:35.311956image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile22
Q130
median39
Q347
95-th percentile55
Maximum68
Range50
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.598847
Coefficient of variation (CV)0.27332976
Kurtosis-0.72082527
Mean38.776778
Median Absolute Deviation (MAD)8
Skewness0.11800361
Sum47424
Variance112.33557
MonotonicityNot monotonic
2024-08-08T20:46:35.437256image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 50
 
0.4%
43 47
 
0.4%
38 45
 
0.3%
40 44
 
0.3%
47 44
 
0.3%
31 40
 
0.3%
33 39
 
0.3%
37 39
 
0.3%
23 37
 
0.3%
44 37
 
0.3%
Other values (41) 801
 
6.0%
(Missing) 12071
90.8%
ValueCountFrequency (%)
18 4
 
< 0.1%
19 15
0.1%
20 10
 
0.1%
21 20
0.2%
22 20
0.2%
23 37
0.3%
24 23
0.2%
25 26
0.2%
26 22
0.2%
27 33
0.2%
ValueCountFrequency (%)
68 1
 
< 0.1%
67 2
 
< 0.1%
66 2
 
< 0.1%
65 2
 
< 0.1%
64 1
 
< 0.1%
63 7
0.1%
62 4
< 0.1%
61 7
0.1%
60 3
< 0.1%
59 5
< 0.1%

gwfg_made
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing12071
Missing (%)90.8%
Memory size104.0 KiB
1.0
1009 
0.0
214 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3669
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1009
 
7.6%
0.0 214
 
1.6%
(Missing) 12071
90.8%

Length

2024-08-08T20:46:35.550370image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:35.633451image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1009
82.5%
0.0 214
 
17.5%

Most occurring characters

ValueCountFrequency (%)
0 1437
39.2%
. 1223
33.3%
1 1009
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1437
39.2%
. 1223
33.3%
1 1009
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1437
39.2%
. 1223
33.3%
1 1009
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1437
39.2%
. 1223
33.3%
1 1009
27.5%

gwfg_missed
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing12071
Missing (%)90.8%
Memory size104.0 KiB
0.0
1046 
1.0
177 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3669
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1046
 
7.9%
1.0 177
 
1.3%
(Missing) 12071
90.8%

Length

2024-08-08T20:46:35.725544image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:35.808614image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1046
85.5%
1.0 177
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0 2269
61.8%
. 1223
33.3%
1 177
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2269
61.8%
. 1223
33.3%
1 177
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2269
61.8%
. 1223
33.3%
1 177
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2269
61.8%
. 1223
33.3%
1 177
 
4.8%

gwfg_blocked
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing12071
Missing (%)90.8%
Memory size104.0 KiB
0.0
1184 
1.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3669
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1184
 
8.9%
1.0 39
 
0.3%
(Missing) 12071
90.8%

Length

2024-08-08T20:46:35.898703image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:46:35.983264image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1184
96.8%
1.0 39
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 2407
65.6%
. 1223
33.3%
1 39
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2407
65.6%
. 1223
33.3%
1 39
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2407
65.6%
. 1223
33.3%
1 39
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2407
65.6%
. 1223
33.3%
1 39
 
1.1%

Interactions

2024-08-08T20:46:21.851714image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:03.612882image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:04.844908image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:06.052848image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:07.374201image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:08.826885image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:10.056059image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:11.314535image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:12.552175image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:13.995887image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:15.269356image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:16.546232image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:17.855472image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:19.328914image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:20.587991image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:21.932793image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:03.689952image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:04.921989image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:06.135923image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:07.458308image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:08.904959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:10.134134image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:11.392610image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:12.631251image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:14.075962image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:15.349431image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:16.629545image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:17.935843image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:19.406995image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:20.667490image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:22.010864image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:03.768023image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:04.999065image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:06.220000image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:07.712554image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:08.980116image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:10.210207image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:11.470684image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:12.707372image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:14.164045image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:15.427510image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:16.709780image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:18.011919image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:19.485072image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:20.746567image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:22.101947image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:03.856903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:05.087334image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:06.311083image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:07.801641image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:09.067117image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:10.303737image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:11.558766image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:12.798465image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:14.257133image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:15.516596image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:16.802878image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:18.107008image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:19.577159image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:20.838654image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:22.193589image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:03.947484image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:05.174421image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:06.405171image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:07.893728image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:09.153196image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:10.389818image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:11.647851image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:12.884552image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:14.345219image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:15.610501image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:16.899976image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:18.198416image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:19.669086image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:20.928741image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:22.270664image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:04.025429image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:05.251493image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:06.487395image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:07.973808image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:09.227270image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:10.474899image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:11.724928image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:12.963627image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:14.423546image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:15.698597image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:16.980377image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:18.284504image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:19.750164image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:21.009829image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:22.355922image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:04.105505image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:05.330572image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:06.577904image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:08.057889image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:09.318353image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:10.554976image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:11.807009image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:13.228881image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:14.506623image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:15.781680image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:17.068465image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:18.371585image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:19.837250image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:21.091903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:22.439998image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:04.187656image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:05.411648image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:06.661980image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:08.141974image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:09.397425image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:10.637055image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:11.885084image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:13.307953image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:14.589703image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:15.865767image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:17.152543image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:18.458766image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:19.922333image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:21.175980image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:22.536085image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:04.266728image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:05.489721image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:06.748060image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:08.230066image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:09.477499image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:10.717141image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:11.964158image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:13.388026image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:14.673782image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:15.944847image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:17.239622image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:18.555862image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:20.006415image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:21.268064image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:22.619162image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:04.347181image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:05.571146image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:06.834642image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:08.315149image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:09.553570image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:10.799217image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:12.047240image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:13.469101image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:14.754855image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:16.031212image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:17.324699image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:18.828537image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:20.091500image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:21.355143image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:22.708243image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:04.429503image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:05.648828image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:06.916719image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:08.397235image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:09.639653image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:10.879286image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:12.125323image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:13.548679image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:14.835934image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:16.107289image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:17.406774image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:18.909617image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:20.174580image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:21.435215image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:22.793320image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:04.512586image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:05.731905image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:07.009879image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:08.485542image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:09.723747image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:10.966368image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:12.210841image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:13.635267image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:14.920013image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:16.194885image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:17.494854image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:19.001792image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:20.262663image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:21.521293image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:22.880399image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:04.592667image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:05.813980image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:07.101927image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:08.571626image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:09.810827image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:11.057464image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:12.300929image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:13.732649image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:15.011100image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:16.286975image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:17.587943image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:19.081866image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:20.339740image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:21.604369image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:22.967480image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:04.680747image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:05.893054image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:07.197016image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:08.655709image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:09.898911image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:11.149359image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:12.392017image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:13.822730image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:15.102189image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:16.378064image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:17.684039image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:19.160942image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:20.421822image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:21.684442image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:23.056066image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:04.762829image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:05.971773image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:07.284111image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:08.740795image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:09.977986image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:11.231450image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:12.472099image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:13.908808image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:15.184273image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:16.460141image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:17.770226image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:19.241022image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:20.501900image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:46:21.766516image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-08-08T20:46:36.260531image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
fg_attfg_blockedfg_blocked_distancefg_longfg_madefg_made_0_19fg_made_20_29fg_made_30_39fg_made_40_49fg_made_50_59fg_made_60_fg_made_distancefg_missedfg_missed_20_29fg_missed_30_39fg_missed_40_49fg_missed_50_59fg_missed_60_fg_missed_distancefg_pctgwfg_attgwfg_blockedgwfg_distancegwfg_madegwfg_missedpat_attpat_blockedpat_madepat_missedpat_pctpositionposition_groupseasonseason_typeteamweek
fg_att1.0000.0780.0960.3290.8430.0740.3620.2310.3500.1130.0450.7870.1610.0400.0860.1210.1120.0560.254-0.1460.1660.0000.0610.0000.000-0.1840.004-0.1570.0180.0310.0100.0010.0220.0000.008-0.027
fg_blocked0.0781.0000.9710.0000.0890.0000.0350.0310.0200.0050.0000.0590.0090.0000.0000.0040.0000.0000.0000.2630.0480.7120.0820.2890.0000.0070.0150.0000.0000.0000.0000.0000.0270.0000.0320.000
fg_blocked_distance0.0960.9711.000-0.012-0.0890.000-0.0480.015-0.0400.0000.000-0.0810.0000.0000.0260.0000.0120.000-0.025-0.3170.1740.7790.0770.3050.000-0.0020.034-0.0120.0000.0000.0000.000-0.0220.0000.028-0.016
fg_long0.3290.000-0.0121.0000.3930.152-0.2950.2880.5390.4250.7030.6890.0180.0000.0300.0160.0000.015-0.0330.0700.0550.1320.5130.1250.082-0.0820.021-0.0850.0000.0010.0000.0000.1460.0000.029-0.029
fg_made0.8430.089-0.0890.3931.0000.1170.4310.2840.4150.1400.0590.9380.1900.0480.1320.1310.1310.051-0.2170.3630.1220.204-0.0520.4480.373-0.1250.000-0.1310.0000.0180.0330.0000.0540.0000.014-0.021
fg_made_0_190.0740.0000.0000.1520.1171.0000.0230.0270.0270.0230.0000.0490.0000.0140.0200.0000.0070.0000.0000.0580.0610.0000.2490.0440.0280.0040.0360.0000.0000.0000.0000.0000.0520.0270.0000.012
fg_made_20_290.3620.035-0.048-0.2950.4310.0231.0000.064-0.1250.0320.0000.1890.0500.0000.0300.0320.0340.000-0.1020.1690.0450.000-0.3590.0400.023-0.0550.032-0.0630.009-0.0050.0060.000-0.0450.0060.003-0.006
fg_made_30_390.2310.0310.0150.2880.2840.0270.0641.0000.0620.0400.0000.2830.0570.0140.0330.0450.0380.0260.0540.1670.0540.0180.2320.1730.1600.0270.0120.0230.0170.0120.0000.0000.0100.0000.0000.000
fg_made_40_490.3500.020-0.0400.5390.4150.027-0.1250.0621.0000.0240.0000.5500.0470.0000.0400.0340.0220.000-0.0930.1620.0570.1010.2360.1420.100-0.0680.004-0.0620.0000.0100.0000.0000.0280.0000.000-0.020
fg_made_50_590.1130.0050.0000.4250.1400.0230.0320.0400.0241.0000.0160.2740.0320.0000.0290.0230.0070.0000.0260.0750.0420.0110.2630.0630.0320.0220.0000.0190.0000.0060.0000.0000.0780.0100.0310.016
fg_made_60_0.0450.0000.0000.7030.0590.0000.0000.0000.0000.0161.0000.1220.0000.0000.0040.0000.0000.0000.0000.0160.0260.0000.3950.0000.0000.0000.0000.0000.0000.0000.0000.0000.0480.0000.0310.000
fg_made_distance0.7870.059-0.0810.6890.9380.0490.1890.2830.5500.2740.1221.0000.1320.0250.0970.0920.0860.025-0.2100.3470.1170.1690.1440.3700.306-0.1240.000-0.1290.0000.0170.0140.0000.0960.0000.017-0.026
fg_missed0.1610.0090.0000.0180.1900.0000.0500.0570.0470.0320.0000.1321.0000.1460.3490.4760.4040.1460.9300.7160.0450.0350.1160.4910.5570.0230.0140.0240.0150.0230.0110.0000.0400.0000.0240.000
fg_missed_20_290.0400.0000.0000.0000.0480.0140.0000.0140.0000.0000.0000.0250.1461.0000.0000.0000.0000.0000.2320.1330.0110.0000.0440.0640.0840.0000.0000.0000.0000.0000.0200.0000.0340.0000.0000.009
fg_missed_30_390.0860.0000.0260.0300.1320.0200.0300.0330.0400.0290.0040.0970.3490.0001.0000.0100.0080.0000.5520.3110.0000.0000.0660.1340.1590.0280.0000.0360.0160.0050.0200.0000.0630.0000.0400.000
fg_missed_40_490.1210.0040.0000.0160.1310.0000.0320.0450.0340.0230.0000.0920.4760.0000.0101.0000.0160.0180.5130.3940.0280.0000.0920.2770.3130.0190.0000.0200.0000.0000.0000.0000.0470.0140.0110.000
fg_missed_50_590.1120.0000.0120.0000.1310.0070.0340.0380.0220.0070.0000.0860.4040.0000.0080.0161.0000.0050.5280.3510.0260.0000.1940.3180.3560.0160.0000.0130.0000.0000.0150.0000.0150.0000.0430.000
fg_missed_60_0.0560.0000.0000.0150.0510.0000.0000.0260.0000.0000.0000.0250.1460.0000.0000.0180.0051.0000.3070.1400.0480.0000.6480.2440.2750.0000.0000.0000.0000.0000.0540.0000.0140.0030.0330.018
fg_missed_distance0.2540.000-0.025-0.033-0.2170.000-0.1020.054-0.0930.0260.000-0.2100.9300.2320.5520.5130.5280.3071.000-0.8900.0450.0000.2300.5000.567-0.0470.000-0.0500.0240.0000.0000.000-0.0590.0000.023-0.008
fg_pct-0.1460.263-0.3170.0700.3630.0580.1690.1670.1620.0750.0160.3470.7160.1330.3110.3940.3510.140-0.8901.0000.1130.293-0.2210.6760.5910.0310.0000.0380.0140.0060.0300.0000.0870.0000.0110.005
gwfg_att0.1660.0480.1740.0550.1220.0610.0450.0540.0570.0420.0260.1170.0450.0110.0000.0280.0260.0480.0450.1131.0000.1630.0000.0000.0000.0570.0000.0570.0000.0000.0000.0000.0330.0000.0330.018
gwfg_blocked0.0000.7120.7790.1320.2040.0000.0000.0180.1010.0110.0000.1690.0350.0000.0000.0000.0000.0000.0000.2930.1631.0000.0790.3620.0620.0000.0000.0000.0000.0000.1520.0000.0770.0000.0000.043
gwfg_distance0.0610.0820.0770.513-0.0520.249-0.3590.2320.2360.2630.3950.1440.1160.0440.0660.0920.1940.6480.230-0.2210.0000.0791.0000.3710.3440.0300.022-0.0070.0160.0080.0000.0000.0970.0170.079-0.035
gwfg_made0.0000.2890.3050.1250.4480.0440.0400.1730.1420.0630.0000.3700.4910.0640.1340.2770.3180.2440.5000.6760.0000.3620.3711.0000.8900.0000.0610.0000.0000.0540.0490.0000.0000.0000.0000.000
gwfg_missed0.0000.0000.0000.0820.3730.0280.0230.1600.1000.0320.0000.3060.5570.0840.1590.3130.3560.2750.5670.5910.0000.0620.3440.8901.0000.0000.0370.0000.0000.0810.0000.0000.0000.0000.0000.000
pat_att-0.1840.007-0.002-0.082-0.1250.004-0.0550.027-0.0680.0220.000-0.1240.0230.0000.0280.0190.0160.000-0.0470.0310.0570.0000.0300.0000.0001.0000.0840.9770.049-0.0650.0000.0000.0430.0260.0540.012
pat_blocked0.0040.0150.0340.0210.0000.0360.0320.0120.0040.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0610.0370.0841.0000.0410.0000.3400.0310.0280.0250.0420.0000.030
pat_made-0.1570.000-0.012-0.085-0.1310.000-0.0630.023-0.0620.0190.000-0.1290.0240.0000.0360.0200.0130.000-0.0500.0380.0570.000-0.0070.0000.0000.9770.0411.0000.0540.1310.0000.000-0.0020.0260.0510.009
pat_missed0.0180.0000.0000.0000.0000.0000.0090.0170.0000.0000.0000.0000.0150.0000.0160.0000.0000.0000.0240.0140.0000.0000.0160.0000.0000.0490.0000.0541.0000.8630.0370.0340.1280.0430.0350.022
pat_pct0.0310.0000.0000.0010.0180.000-0.0050.0120.0100.0060.0000.0170.0230.0000.0050.0000.0000.0000.0000.0060.0000.0000.0080.0540.081-0.0650.3400.1310.8631.0000.0450.043-0.1700.0440.022-0.014
position0.0100.0000.0000.0000.0330.0000.0060.0000.0000.0000.0000.0140.0110.0200.0200.0000.0150.0540.0000.0300.0000.1520.0000.0490.0000.0000.0310.0000.0370.0451.0001.0000.0010.0000.0290.009
position_group0.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0340.0431.0001.0000.0000.0000.0180.000
season0.0220.027-0.0220.1460.0540.052-0.0450.0100.0280.0780.0480.0960.0400.0340.0630.0470.0150.014-0.0590.0870.0330.0770.0970.0000.0000.0430.025-0.0020.128-0.1700.0010.0001.0000.0000.0000.011
season_type0.0000.0000.0000.0000.0000.0270.0060.0000.0000.0100.0000.0000.0000.0000.0000.0140.0000.0030.0000.0000.0000.0000.0170.0000.0000.0260.0420.0260.0430.0440.0000.0000.0001.0000.0870.925
team0.0080.0320.0280.0290.0140.0000.0030.0000.0000.0310.0310.0170.0240.0000.0400.0110.0430.0330.0230.0110.0330.0000.0790.0000.0000.0540.0000.0510.0350.0220.0290.0180.0000.0871.0000.000
week-0.0270.000-0.016-0.029-0.0210.012-0.0060.000-0.0200.0160.000-0.0260.0000.0090.0000.0000.0000.018-0.0080.0050.0180.043-0.0350.0000.0000.0120.0300.0090.022-0.0140.0090.0000.0110.9250.0001.000

Missing values

2024-08-08T20:46:23.232226image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-08T20:46:23.924863image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-08T20:46:24.412069image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

seasonweekseason_typeplayer_idteamplayer_nameplayer_display_namepositionposition_groupheadshot_urlfg_madefg_attfg_missedfg_blockedfg_longfg_pctfg_made_0_19fg_made_20_29fg_made_30_39fg_made_40_49fg_made_50_59fg_made_60_fg_missed_0_19fg_missed_20_29fg_missed_30_39fg_missed_40_49fg_missed_50_59fg_missed_60_fg_made_listfg_missed_listfg_blocked_listfg_made_distancefg_missed_distancefg_blocked_distancepat_madepat_attpat_missedpat_blockedpat_pctgwfg_attgwfg_distancegwfg_madegwfg_missedgwfg_blocked
019997REG00-0000108PHINaNDavid AkersKSPECNaN1.010.00.053.01.0000.00.00.00.01.00.00.00.00.00.00.00.053NaNNaN53.00.00.0NaN0NaNNaNNaN0NaNNaNNaNNaN
119998REG00-0000108PHINaNDavid AkersKSPECNaN0.011.00.0NaN0.0000.00.00.00.00.00.00.00.00.00.01.00.0NaN59NaN0.059.00.0NaN0NaNNaNNaN159.00.01.00.0
2199911REG00-0000108PHINaNDavid AkersKSPECNaN1.010.00.048.01.0000.00.00.01.00.00.00.00.00.00.00.00.048NaNNaN48.00.00.02.020.00.01.00NaNNaNNaNNaN
3199915REG00-0000108PHINaNDavid AkersKSPECNaN0.011.00.0NaN0.0000.00.00.00.00.00.00.00.00.00.01.00.0NaN51NaN0.051.00.02.020.00.01.00NaNNaNNaNNaN
4199917REG00-0000108PHINaNDavid AkersKSPECNaN1.010.00.046.01.0000.00.00.01.00.00.00.00.00.00.00.00.046NaNNaN46.00.00.02.020.00.01.00NaNNaNNaNNaN
519991REG00-0000282ATLNaNMorten AndersenKSPECNaN0.022.00.0NaN0.0000.00.00.00.00.00.00.00.02.00.00.00.0NaN35;39NaN0.074.00.02.020.00.01.00NaNNaNNaNNaN
619992REG00-0000282ATLNaNMorten AndersenKSPECNaN0.011.00.0NaN0.0000.00.00.00.00.00.00.00.00.01.00.00.0NaN49NaN0.049.00.01.010.00.01.00NaNNaNNaNNaN
719993REG00-0000282ATLNaNMorten AndersenKSPECNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.010.00.01.00NaNNaNNaNNaN
819994REG00-0000282ATLNaNMorten AndersenKSPECNaN2.031.00.041.00.6670.00.01.01.00.00.00.00.01.00.00.00.041;3533NaN76.033.00.01.010.00.01.00NaNNaNNaNNaN
919995REG00-0000282ATLNaNMorten AndersenKSPECNaN2.020.00.044.01.0000.00.01.01.00.00.00.00.00.00.00.00.036;44NaNNaN80.00.00.02.020.00.01.0144.01.00.00.0
seasonweekseason_typeplayer_idteamplayer_nameplayer_display_namepositionposition_groupheadshot_urlfg_madefg_attfg_missedfg_blockedfg_longfg_pctfg_made_0_19fg_made_20_29fg_made_30_39fg_made_40_49fg_made_50_59fg_made_60_fg_missed_0_19fg_missed_20_29fg_missed_30_39fg_missed_40_49fg_missed_50_59fg_missed_60_fg_made_listfg_missed_listfg_blocked_listfg_made_distancefg_missed_distancefg_blocked_distancepat_madepat_attpat_missedpat_blockedpat_pctgwfg_attgwfg_distancegwfg_madegwfg_missedgwfg_blocked
1328420238REG00-0038905NOB.GrupeBlake GrupeKSPEChttps://static.www.nfl.com/image/private/f_auto,q_auto/league/bvgadzdytl0cif3xxukt1.010.00.027.01.0000.01.00.00.00.00.00.00.00.00.00.00.027NaNNaN27.00.00.05.050.00.01.00NaNNaNNaNNaN
1328520239REG00-0038905NOB.GrupeBlake GrupeKSPEChttps://static.www.nfl.com/image/private/f_auto,q_auto/league/bvgadzdytl0cif3xxukt1.021.00.055.00.5000.00.00.00.01.00.00.00.00.01.00.00.05547NaN55.047.00.03.030.00.01.00NaNNaNNaNNaN
13286202310REG00-0038905NOB.GrupeBlake GrupeKSPEChttps://static.www.nfl.com/image/private/f_auto,q_auto/league/bvgadzdytl0cif3xxukt1.010.00.048.01.0000.00.00.01.00.00.00.00.00.00.00.00.048NaNNaN48.00.00.0NaN0NaNNaNNaN0NaNNaNNaNNaN
13287202312REG00-0038905NOB.GrupeBlake GrupeKSPEChttps://static.www.nfl.com/image/private/f_auto,q_auto/league/bvgadzdytl0cif3xxukt5.061.00.052.00.8330.01.01.02.01.00.00.00.00.00.01.00.025;52;41;45;3954NaN202.054.00.0NaN0NaNNaNNaN0NaNNaNNaNNaN
13288202313REG00-0038905NOB.GrupeBlake GrupeKSPEChttps://static.www.nfl.com/image/private/f_auto,q_auto/league/bvgadzdytl0cif3xxuktNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.040.00.01.00NaNNaNNaNNaN
13289202314REG00-0038905NOB.GrupeBlake GrupeKSPEChttps://static.www.nfl.com/image/private/f_auto,q_auto/league/bvgadzdytl0cif3xxukt0.011.00.0NaN0.0000.00.00.00.00.00.00.01.00.00.00.00.0NaN29NaN0.029.00.04.040.00.01.00NaNNaNNaNNaN
13290202315REG00-0038905NOB.GrupeBlake GrupeKSPEChttps://static.www.nfl.com/image/private/f_auto,q_auto/league/bvgadzdytl0cif3xxukt1.010.00.050.01.0000.00.00.00.01.00.00.00.00.00.00.00.050NaNNaN50.00.00.03.030.00.01.00NaNNaNNaNNaN
13291202316REG00-0038905NOB.GrupeBlake GrupeKSPEChttps://static.www.nfl.com/image/private/f_auto,q_auto/league/bvgadzdytl0cif3xxuktNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.020.00.01.00NaNNaNNaNNaN
13292202317REG00-0038905NOB.GrupeBlake GrupeKSPEChttps://static.www.nfl.com/image/private/f_auto,q_auto/league/bvgadzdytl0cif3xxukt3.030.00.045.01.0000.01.01.01.00.00.00.00.00.00.00.00.045;28;38NaNNaN111.00.00.02.020.00.01.00NaNNaNNaNNaN
13293202318REG00-0038905NOB.GrupeBlake GrupeKSPEChttps://static.www.nfl.com/image/private/f_auto,q_auto/league/bvgadzdytl0cif3xxukt2.020.00.024.01.0000.02.00.00.00.00.00.00.00.00.00.00.024;24NaNNaN48.00.00.06.060.00.01.00NaNNaNNaNNaN